| # 📈 Stock Price Forecasting - DataSynthis ML Job Task | |
| This repository contains implementations of **time-series forecasting** for stock prices using both **traditional statistical models (ARIMA, Prophet)** and **deep learning (LSTM)**. | |
| The project demonstrates model comparison, rolling-window evaluation, and deployment to Hugging Face Hub. | |
| --- | |
| ## Project Overview | |
| - **Dataset**: Daily stock price dataset (closing prices). | |
| - **Models Implemented**: | |
| - ARIMA (AutoRegressive Integrated Moving Average) | |
| - Prophet (Additive Time Series Forecasting by Meta) | |
| - LSTM (Long Short-Term Memory Neural Network) | |
| - **Evaluation**: | |
| - Rolling-window forecasts | |
| - Metrics: RMSE, MAPE | |
| - **Deployment**: | |
| - Models and results shared on Hugging Face Hub. | |
| --- | |
| ## Repository Contents | |
| - `lstm_model.h5` – Trained LSTM model | |
| - `scaler.pkl` – Scaler used for preprocessing | |
| - `performance_summary.csv` – Comparison of ARIMA, Prophet, and LSTM performance | |
| - `stock_forecasting_notebook.ipynb` – Full notebook with preprocessing, training, evaluation, and plots | |
| - `upload_to_hf.py` – Script for uploading to Hugging Face Hub | |
| --- | |
| ## Quick start | |
| 1. Create and activate a python environment (recommended: conda or venv) | |
| ```bash | |
| python -m venv venv | |
| source venv/bin/activate # Linux/macOS | |
| venv\Scripts\activate # Windows | |
| pip install -r requirements.txt | |
| ``` | |
| 2. Start Jupyter and open the notebook: | |
| ```bash | |
| jupyter notebook stock_forecasting_notebook.ipynb | |
| ``` | |
| 3. The notebook contains cells to download real stock data via `yfinance` (if you have internet) or use the included `sample_stock.csv` for an offline demo. | |
| ## Hugging Face deployment (notes) | |
| - Use `upload_to_hf.py` to push saved model files to the HF repo `DataSynthis_ML_JobTask` after creating it on the Hugging Face website (or the script will create the repo for you if you provide a valid token). | |
| - Create a HF token at https://huggingface.co/settings/tokens and set environment variable `HF_TOKEN` or pass `--token` to the script. | |
| ## Results | |
| The performance of the three models on stock price forecasting is summarized below: | |
| | Model | RMSE | MAPE (%) | | |
| |---------|--------|----------| | |
| | ARIMA | 3.3748 | 1.8973 | | |
| | Prophet | 4.7650 | 3.1859 | | |
| | LSTM | 2.0890 | 1.2516 | | |
| ### Key Insights | |
| - **LSTM** achieved the **lowest RMSE and MAPE**, showing the best accuracy. | |
| - **ARIMA** performed reasonably well, but less effective with non-linear trends. | |
| - **Prophet** captured trends and seasonality but had higher errors. | |
| - Overall, **LSTM is the most reliable model** for this task. | |